➢ 开源机器学习库：https://scikit-learn.org/stable/index.html
➢ 涵盖分类、回归、聚类、降维、模型选择、数据预处理六大模块
降低机器学习实践门槛
➢ 涵盖机器学习全流程操作
➢ 将复杂的数学计算集成为简单的函数
➢ 提供了众多公开数据集和学习案例
# 导入数据
from sklearn.datasets import load_breast_cancer
# 数据以字典的形式呈现
data=load_breast_cancer()
data
{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,
1.189e-01],
[2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,
8.902e-02],
[1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,
8.758e-02],
...,
[1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,
7.820e-02],
[2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,
1.240e-01],
[7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,
7.039e-02]]),
'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,
1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),
'frame': None,
'target_names': array(['malignant', 'benign'], dtype='<U9'),
'DESCR': '.. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n :Number of Instances: 569\n\n :Number of Attributes: 30 numeric, predictive attributes and the class\n\n :Attribute Information:\n - radius (mean of distances from center to points on the perimeter)\n - texture (standard deviation of gray-scale values)\n - perimeter\n - area\n - smoothness (local variation in radius lengths)\n - compactness (perimeter^2 / area - 1.0)\n - concavity (severity of concave portions of the contour)\n - concave points (number of concave portions of the contour)\n - symmetry\n - fractal dimension ("coastline approximation" - 1)\n\n The mean, standard error, and "worst" or largest (mean of the three\n worst/largest values) of these features were computed for each image,\n resulting in 30 features. For instance, field 0 is Mean Radius, field\n 10 is Radius SE, field 20 is Worst Radius.\n\n - class:\n - WDBC-Malignant\n - WDBC-Benign\n\n :Summary Statistics:\n\n ===================================== ====== ======\n Min Max\n ===================================== ====== ======\n radius (mean): 6.981 28.11\n texture (mean): 9.71 39.28\n perimeter (mean): 43.79 188.5\n area (mean): 143.5 2501.0\n smoothness (mean): 0.053 0.163\n compactness (mean): 0.019 0.345\n concavity (mean): 0.0 0.427\n concave points (mean): 0.0 0.201\n symmetry (mean): 0.106 0.304\n fractal dimension (mean): 0.05 0.097\n radius (standard error): 0.112 2.873\n texture (standard error): 0.36 4.885\n perimeter (standard error): 0.757 21.98\n area (standard error): 6.802 542.2\n smoothness (standard error): 0.002 0.031\n compactness (standard error): 0.002 0.135\n concavity (standard error): 0.0 0.396\n concave points (standard error): 0.0 0.053\n symmetry (standard error): 0.008 0.079\n fractal dimension (standard error): 0.001 0.03\n radius (worst): 7.93 36.04\n texture (worst): 12.02 49.54\n perimeter (worst): 50.41 251.2\n area (worst): 185.2 4254.0\n smoothness (worst): 0.071 0.223\n compactness (worst): 0.027 1.058\n concavity (worst): 0.0 1.252\n concave points (worst): 0.0 0.291\n symmetry (worst): 0.156 0.664\n fractal dimension (worst): 0.055 0.208\n ===================================== ====== ======\n\n :Missing Attribute Values: None\n\n :Class Distribution: 212 - Malignant, 357 - Benign\n\n :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n :Donor: Nick Street\n\n :Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass. They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree\nConstruction Via Linear Programming." Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree. Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: "Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n.. topic:: References\n\n - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n San Jose, CA, 1993.\n - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n July-August 1995.\n - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n 163-171.',
'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness', 'mean compactness', 'mean concavity',
'mean concave points', 'mean symmetry', 'mean fractal dimension',
'radius error', 'texture error', 'perimeter error', 'area error',
'smoothness error', 'compactness error', 'concavity error',
'concave points error', 'symmetry error',
'fractal dimension error', 'worst radius', 'worst texture',
'worst perimeter', 'worst area', 'worst smoothness',
'worst compactness', 'worst concavity', 'worst concave points',
'worst symmetry', 'worst fractal dimension'], dtype='<U23'),
'filename': 'breast_cancer.csv',
'data_module': 'sklearn.datasets.data'}
# 查看键
data.keys()
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])
data.filename
'breast_cancer.csv'
# 访问数据--字典方式
data['data']
array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,
1.189e-01],
[2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,
8.902e-02],
[1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,
8.758e-02],
...,
[1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,
7.820e-02],
[2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,
1.240e-01],
[7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,
7.039e-02]])
➢ train_test_split函数根据传入的数据，分别将传入的数据划分为训练集和测试集。
➢ 如果传入的是1组数据，那么生成的就是这一组数据随机划分后训练集和测试集，总共2组。如果传入的是
2组数据，则生成的训练集和测试集分别2组，总共4组。
➢ train_test_split是最常用的数据划分方法，在model_selection模块中还提供了其他数据集划分的函数，
如PredefinedSplit，ShuffleSplit等。
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
import pandas as pd
data=load_iris()
# data['data'] 训练数据
# data['target'], 训练标签
# train_size=0.2 分割比例，数据分割为训练集和测试集
x_train,x_test,y_train,y_train=train_test_split(
data['data'],data['target'],
test_size=0.2)
# 统计数据频率
pd.value_counts(y_train)
1 13 0 10 2 7 dtype: int64
# stratify=data['target'] 按照数据标签合理分割数据
x_train,x_test,y_train,y_train=train_test_split(
data['data'],data['target'],
test_size=0.2,stratify=data['target'])
# 统计数据频率
pd.value_counts(y_train)
2 10 0 10 1 10 dtype: int64
➢ 在数据分析过程中，各类特征处理相关的操作都需要对训练集和测试集分开操作，需要将训练集的操作规
则，权重系数等应用到测试集中。
➢ 如果使用pandas，则应用至测试集的过程相对烦琐，使用sklearn转换器可以解决这一困扰。
from sklearn.preprocessing import MinMaxScaler
xtrain,xtest,ytrain,ytest=train_test_split(
data['data'],data['target'],
test_size=0.2,stratify=data['target'])
构建转换器、执行转换器分步完成
# 构建转换器
tf=MinMaxScaler().fit(xtrain)
# 执行转换器
tf.transform(xtrain)
array([[0.36111111, 0.29166667, 0.55357143, 0.5 ],
[0.25 , 0.875 , 0.07142857, 0. ],
[0.36111111, 0.20833333, 0.5 , 0.41666667],
[0.19444444, 0.5 , 0.01785714, 0.04166667],
[0.47222222, 0.375 , 0.60714286, 0.58333333],
[0.91666667, 0.41666667, 0.98214286, 0.83333333],
[0.80555556, 0.41666667, 0.83928571, 0.625 ],
[0.55555556, 0.375 , 0.80357143, 0.70833333],
[0.41666667, 0.29166667, 0.5 , 0.45833333],
[0.25 , 0.58333333, 0.05357143, 0.04166667],
[0.55555556, 0.20833333, 0.69642857, 0.75 ],
[0.08333333, 0.45833333, 0.07142857, 0.04166667],
[0.44444444, 0.41666667, 0.71428571, 0.70833333],
[0.05555556, 0.125 , 0.03571429, 0.08333333],
[0.61111111, 0.41666667, 0.78571429, 0.70833333],
[0.72222222, 0.45833333, 0.71428571, 0.91666667],
[0.72222222, 0.45833333, 0.76785714, 0.83333333],
[0.19444444, 0.41666667, 0.08928571, 0.04166667],
[0.41666667, 0.33333333, 0.71428571, 0.95833333],
[0.61111111, 0.41666667, 0.73214286, 0.79166667],
[0.25 , 0.625 , 0.07142857, 0.04166667],
[0.61111111, 0.41666667, 0.83928571, 0.875 ],
[0.02777778, 0.5 , 0.03571429, 0.04166667],
[0.66666667, 0.45833333, 0.80357143, 0.95833333],
[0.38888889, 0.25 , 0.42857143, 0.375 ],
[0.11111111, 0.5 , 0.03571429, 0.04166667],
[0.33333333, 0.91666667, 0.05357143, 0.04166667],
[0.02777778, 0.41666667, 0.03571429, 0.04166667],
[0.16666667, 0.45833333, 0.07142857, 0. ],
[0.02777778, 0.375 , 0.05357143, 0.04166667],
[0.27777778, 0.70833333, 0.07142857, 0.04166667],
[0.94444444, 0.75 , 1. , 0.875 ],
[0.5 , 0.25 , 0.80357143, 0.54166667],
[0.44444444, 0.5 , 0.66071429, 0.70833333],
[0.13888889, 0.45833333, 0.08928571, 0.04166667],
[0.22222222, 0.625 , 0.05357143, 0.08333333],
[0.52777778, 0.58333333, 0.76785714, 0.91666667],
[0.5 , 0.41666667, 0.67857143, 0.70833333],
[0.47222222, 0.29166667, 0.71428571, 0.625 ],
[0.5 , 0.33333333, 0.51785714, 0.5 ],
[0.58333333, 0.375 , 0.57142857, 0.5 ],
[0.33333333, 0.20833333, 0.51785714, 0.5 ],
[0.55555556, 0.33333333, 0.71428571, 0.58333333],
[0.58333333, 0.5 , 0.75 , 0.91666667],
[1. , 0.75 , 0.94642857, 0.79166667],
[0.36111111, 0.41666667, 0.53571429, 0.5 ],
[0.94444444, 0.33333333, 1. , 0.79166667],
[0.22222222, 0.75 , 0.07142857, 0.08333333],
[0.63888889, 0.375 , 0.625 , 0.5 ],
[0.36111111, 0.33333333, 0.67857143, 0.79166667],
[0.58333333, 0.5 , 0.60714286, 0.58333333],
[0.33333333, 0.16666667, 0.48214286, 0.41666667],
[0.80555556, 0.66666667, 0.89285714, 1. ],
[0.52777778, 0.33333333, 0.66071429, 0.70833333],
[0.72222222, 0.5 , 0.82142857, 0.91666667],
[0.41666667, 0.29166667, 0.71428571, 0.75 ],
[0.47222222, 0.41666667, 0.66071429, 0.70833333],
[0.55555556, 0.54166667, 0.64285714, 0.625 ],
[0.63888889, 0.41666667, 0.58928571, 0.54166667],
[0.11111111, 0.5 , 0.08928571, 0.04166667],
[0.41666667, 0.83333333, 0.01785714, 0.04166667],
[0.69444444, 0.41666667, 0.78571429, 0.83333333],
[0.66666667, 0.41666667, 0.69642857, 0.66666667],
[0.38888889, 0.41666667, 0.55357143, 0.45833333],
[0.86111111, 0.33333333, 0.89285714, 0.75 ],
[0.58333333, 0.33333333, 0.80357143, 0.875 ],
[0.22222222, 0.70833333, 0.07142857, 0.125 ],
[0.30555556, 0.58333333, 0.07142857, 0.125 ],
[0.38888889, 1. , 0.07142857, 0.125 ],
[0.52777778, 0.375 , 0.57142857, 0.5 ],
[0.19444444, 0.625 , 0.08928571, 0.20833333],
[0.5 , 0.41666667, 0.625 , 0.54166667],
[0.33333333, 0.125 , 0.51785714, 0.5 ],
[0.36111111, 0.375 , 0.44642857, 0.5 ],
[0.22222222, 0.625 , 0.05357143, 0.04166667],
[0.66666667, 0.54166667, 0.82142857, 0.83333333],
[0.66666667, 0.45833333, 0.58928571, 0.54166667],
[0.58333333, 0.29166667, 0.75 , 0.75 ],
[0.55555556, 0.54166667, 0.875 , 1. ],
[0.66666667, 0.45833333, 0.64285714, 0.58333333],
[0.66666667, 0.41666667, 0.73214286, 0.91666667],
[0.16666667, 0.16666667, 0.39285714, 0.375 ],
[0.47222222, 0.08333333, 0.51785714, 0.375 ],
[0.16666667, 0.45833333, 0.07142857, 0.04166667],
[0.33333333, 0.16666667, 0.46428571, 0.375 ],
[0.75 , 0.5 , 0.64285714, 0.54166667],
[0.08333333, 0.5 , 0.05357143, 0.04166667],
[0.19444444, 0.125 , 0.39285714, 0.375 ],
[0.66666667, 0.20833333, 0.83928571, 0.70833333],
[0.47222222, 0.58333333, 0.60714286, 0.625 ],
[0.22222222, 0.20833333, 0.33928571, 0.41666667],
[0.66666667, 0.54166667, 0.82142857, 1. ],
[0.08333333, 0.58333333, 0.05357143, 0.08333333],
[0.30555556, 0.58333333, 0.10714286, 0.04166667],
[0. , 0.41666667, 0. , 0. ],
[0.55555556, 0.29166667, 0.67857143, 0.70833333],
[0.61111111, 0.5 , 0.71428571, 0.79166667],
[0.83333333, 0.375 , 0.92857143, 0.70833333],
[0.19444444, 0. , 0.42857143, 0.375 ],
[0.25 , 0.29166667, 0.5 , 0.54166667],
[0.13888889, 0.41666667, 0.05357143, 0. ],
[0.22222222, 0.58333333, 0.07142857, 0.04166667],
[0.30555556, 0.70833333, 0.07142857, 0.04166667],
[0.36111111, 0.41666667, 0.60714286, 0.58333333],
[0.41666667, 0.29166667, 0.53571429, 0.375 ],
[0.33333333, 0.625 , 0.03571429, 0.04166667],
[0.22222222, 0.75 , 0.14285714, 0.125 ],
[0.13888889, 0.41666667, 0.05357143, 0.08333333],
[0.69444444, 0.5 , 0.85714286, 0.91666667],
[0.19444444, 0.66666667, 0.05357143, 0.04166667],
[0.72222222, 0.45833333, 0.67857143, 0.58333333],
[0.38888889, 0.20833333, 0.69642857, 0.79166667],
[0.22222222, 0.54166667, 0.10714286, 0.16666667],
[0.52777778, 0.08333333, 0.60714286, 0.58333333],
[0.5 , 0.33333333, 0.64285714, 0.45833333],
[0.16666667, 0.41666667, 0.05357143, 0.04166667],
[0.38888889, 0.75 , 0.10714286, 0.08333333],
[0.44444444, 0.41666667, 0.55357143, 0.58333333],
[0.16666667, 0.66666667, 0.05357143, 0. ],
[0.38888889, 0.33333333, 0.60714286, 0.5 ]])
构建、执行转换器一步完成
MinMaxScaler().fit_transform(xtrain)
array([[0.36111111, 0.29166667, 0.55357143, 0.5 ],
[0.25 , 0.875 , 0.07142857, 0. ],
[0.36111111, 0.20833333, 0.5 , 0.41666667],
[0.19444444, 0.5 , 0.01785714, 0.04166667],
[0.47222222, 0.375 , 0.60714286, 0.58333333],
[0.91666667, 0.41666667, 0.98214286, 0.83333333],
[0.80555556, 0.41666667, 0.83928571, 0.625 ],
[0.55555556, 0.375 , 0.80357143, 0.70833333],
[0.41666667, 0.29166667, 0.5 , 0.45833333],
[0.25 , 0.58333333, 0.05357143, 0.04166667],
[0.55555556, 0.20833333, 0.69642857, 0.75 ],
[0.08333333, 0.45833333, 0.07142857, 0.04166667],
[0.44444444, 0.41666667, 0.71428571, 0.70833333],
[0.05555556, 0.125 , 0.03571429, 0.08333333],
[0.61111111, 0.41666667, 0.78571429, 0.70833333],
[0.72222222, 0.45833333, 0.71428571, 0.91666667],
[0.72222222, 0.45833333, 0.76785714, 0.83333333],
[0.19444444, 0.41666667, 0.08928571, 0.04166667],
[0.41666667, 0.33333333, 0.71428571, 0.95833333],
[0.61111111, 0.41666667, 0.73214286, 0.79166667],
[0.25 , 0.625 , 0.07142857, 0.04166667],
[0.61111111, 0.41666667, 0.83928571, 0.875 ],
[0.02777778, 0.5 , 0.03571429, 0.04166667],
[0.66666667, 0.45833333, 0.80357143, 0.95833333],
[0.38888889, 0.25 , 0.42857143, 0.375 ],
[0.11111111, 0.5 , 0.03571429, 0.04166667],
[0.33333333, 0.91666667, 0.05357143, 0.04166667],
[0.02777778, 0.41666667, 0.03571429, 0.04166667],
[0.16666667, 0.45833333, 0.07142857, 0. ],
[0.02777778, 0.375 , 0.05357143, 0.04166667],
[0.27777778, 0.70833333, 0.07142857, 0.04166667],
[0.94444444, 0.75 , 1. , 0.875 ],
[0.5 , 0.25 , 0.80357143, 0.54166667],
[0.44444444, 0.5 , 0.66071429, 0.70833333],
[0.13888889, 0.45833333, 0.08928571, 0.04166667],
[0.22222222, 0.625 , 0.05357143, 0.08333333],
[0.52777778, 0.58333333, 0.76785714, 0.91666667],
[0.5 , 0.41666667, 0.67857143, 0.70833333],
[0.47222222, 0.29166667, 0.71428571, 0.625 ],
[0.5 , 0.33333333, 0.51785714, 0.5 ],
[0.58333333, 0.375 , 0.57142857, 0.5 ],
[0.33333333, 0.20833333, 0.51785714, 0.5 ],
[0.55555556, 0.33333333, 0.71428571, 0.58333333],
[0.58333333, 0.5 , 0.75 , 0.91666667],
[1. , 0.75 , 0.94642857, 0.79166667],
[0.36111111, 0.41666667, 0.53571429, 0.5 ],
[0.94444444, 0.33333333, 1. , 0.79166667],
[0.22222222, 0.75 , 0.07142857, 0.08333333],
[0.63888889, 0.375 , 0.625 , 0.5 ],
[0.36111111, 0.33333333, 0.67857143, 0.79166667],
[0.58333333, 0.5 , 0.60714286, 0.58333333],
[0.33333333, 0.16666667, 0.48214286, 0.41666667],
[0.80555556, 0.66666667, 0.89285714, 1. ],
[0.52777778, 0.33333333, 0.66071429, 0.70833333],
[0.72222222, 0.5 , 0.82142857, 0.91666667],
[0.41666667, 0.29166667, 0.71428571, 0.75 ],
[0.47222222, 0.41666667, 0.66071429, 0.70833333],
[0.55555556, 0.54166667, 0.64285714, 0.625 ],
[0.63888889, 0.41666667, 0.58928571, 0.54166667],
[0.11111111, 0.5 , 0.08928571, 0.04166667],
[0.41666667, 0.83333333, 0.01785714, 0.04166667],
[0.69444444, 0.41666667, 0.78571429, 0.83333333],
[0.66666667, 0.41666667, 0.69642857, 0.66666667],
[0.38888889, 0.41666667, 0.55357143, 0.45833333],
[0.86111111, 0.33333333, 0.89285714, 0.75 ],
[0.58333333, 0.33333333, 0.80357143, 0.875 ],
[0.22222222, 0.70833333, 0.07142857, 0.125 ],
[0.30555556, 0.58333333, 0.07142857, 0.125 ],
[0.38888889, 1. , 0.07142857, 0.125 ],
[0.52777778, 0.375 , 0.57142857, 0.5 ],
[0.19444444, 0.625 , 0.08928571, 0.20833333],
[0.5 , 0.41666667, 0.625 , 0.54166667],
[0.33333333, 0.125 , 0.51785714, 0.5 ],
[0.36111111, 0.375 , 0.44642857, 0.5 ],
[0.22222222, 0.625 , 0.05357143, 0.04166667],
[0.66666667, 0.54166667, 0.82142857, 0.83333333],
[0.66666667, 0.45833333, 0.58928571, 0.54166667],
[0.58333333, 0.29166667, 0.75 , 0.75 ],
[0.55555556, 0.54166667, 0.875 , 1. ],
[0.66666667, 0.45833333, 0.64285714, 0.58333333],
[0.66666667, 0.41666667, 0.73214286, 0.91666667],
[0.16666667, 0.16666667, 0.39285714, 0.375 ],
[0.47222222, 0.08333333, 0.51785714, 0.375 ],
[0.16666667, 0.45833333, 0.07142857, 0.04166667],
[0.33333333, 0.16666667, 0.46428571, 0.375 ],
[0.75 , 0.5 , 0.64285714, 0.54166667],
[0.08333333, 0.5 , 0.05357143, 0.04166667],
[0.19444444, 0.125 , 0.39285714, 0.375 ],
[0.66666667, 0.20833333, 0.83928571, 0.70833333],
[0.47222222, 0.58333333, 0.60714286, 0.625 ],
[0.22222222, 0.20833333, 0.33928571, 0.41666667],
[0.66666667, 0.54166667, 0.82142857, 1. ],
[0.08333333, 0.58333333, 0.05357143, 0.08333333],
[0.30555556, 0.58333333, 0.10714286, 0.04166667],
[0. , 0.41666667, 0. , 0. ],
[0.55555556, 0.29166667, 0.67857143, 0.70833333],
[0.61111111, 0.5 , 0.71428571, 0.79166667],
[0.83333333, 0.375 , 0.92857143, 0.70833333],
[0.19444444, 0. , 0.42857143, 0.375 ],
[0.25 , 0.29166667, 0.5 , 0.54166667],
[0.13888889, 0.41666667, 0.05357143, 0. ],
[0.22222222, 0.58333333, 0.07142857, 0.04166667],
[0.30555556, 0.70833333, 0.07142857, 0.04166667],
[0.36111111, 0.41666667, 0.60714286, 0.58333333],
[0.41666667, 0.29166667, 0.53571429, 0.375 ],
[0.33333333, 0.625 , 0.03571429, 0.04166667],
[0.22222222, 0.75 , 0.14285714, 0.125 ],
[0.13888889, 0.41666667, 0.05357143, 0.08333333],
[0.69444444, 0.5 , 0.85714286, 0.91666667],
[0.19444444, 0.66666667, 0.05357143, 0.04166667],
[0.72222222, 0.45833333, 0.67857143, 0.58333333],
[0.38888889, 0.20833333, 0.69642857, 0.79166667],
[0.22222222, 0.54166667, 0.10714286, 0.16666667],
[0.52777778, 0.08333333, 0.60714286, 0.58333333],
[0.5 , 0.33333333, 0.64285714, 0.45833333],
[0.16666667, 0.41666667, 0.05357143, 0.04166667],
[0.38888889, 0.75 , 0.10714286, 0.08333333],
[0.44444444, 0.41666667, 0.55357143, 0.58333333],
[0.16666667, 0.66666667, 0.05357143, 0. ],
[0.38888889, 0.33333333, 0.60714286, 0.5 ]])
聚类的概念
聚类的要求
聚类与分类的区别在于聚类不依赖于预先定义的类，没有预定义的类和样本——聚类是一种无监督的数据挖掘任务
➢ 上表总共列出了6种评价的方法，其中前4种方法均需要真实值的配合才能够评价聚类算法的优劣，后2种
则不需要真实值的配合。但是前4种方法评价的效果更具有说服力，并且在实际运行的过程中在有真实值
做参考的情况下，聚类方法的评价可以等同于分类算法的评价。
➢ 除了轮廓系数以外的评价方法，在不考虑业务场景的情况下都是得分越高，其效果越好，最高分值均为1
。而轮廓系数则需要判断不同类别数目的情况下其轮廓系数的走势，寻找最优的聚类数目。
➢ 在具备真实值作为参考的情况下，几种方法均可以很好地评估聚类模型。在没有真实值作为参考的时候，
轮廓系数评价方法和Calinski-Harabasz指数评价方法可以结合使用。
from sklearn.cluster import KMeans
# 设置聚类方式 n_clusters=3 表示聚成3类
km=KMeans(n_clusters=3)
# 执行聚类操作 data['data'] 表示需要聚类的数据
km.fit(data['data'])
# 查看聚类后的数据标签
km.labels_
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2,
2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2,
2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1])
data['target']
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
# 访问数据聚类中心
km.cluster_centers_
array([[5.006 , 3.428 , 1.462 , 0.246 ],
[5.9016129 , 2.7483871 , 4.39354839, 1.43387097],
[6.85 , 3.07368421, 5.74210526, 2.07105263]])
from sklearn.metrics import adjusted_rand_score
# 评估模型（有数据标签）
# help(adjusted_rand_score)
# data['target'] 表示实际标签
# km.labeks_ 表示预测标签
adjusted_rand_score(data['target'],km.labels_)
0.7302382722834697
from sklearn.metrics import silhouette_score\
# 评估模型（无数据标签啊）
# silhouette_score?
# data['data'] 表示聚类数据源
# km.labels_ 表示聚类标签
silhouette_score(data['data'],km.labels_)
0.5528190123564095
from sklearn.datasets import load_iris
# return_X_y=True 表示仅仅放回数据和标签
data = load_iris(return_X_y=True)
# 数据以元组的方式返回
# type(data)
data[1]
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
from sklearn.model_selection import train_test_split
# 将数据分为训练集和测试集
# data[0] 表示数据,
# data[1] 表示标签
x = data[0]
y = data[1]
x_train,x_test,y_train,y_test=train_test_split(
x,y,test_size=0.2,
stratify=y)
y_train
array([0, 0, 2, 1, 2, 0, 1, 0, 0, 0, 2, 1, 0, 2, 0, 0, 0, 2, 1, 0, 2, 2,
2, 1, 0, 2, 2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1,
2, 2, 1, 2, 2, 1, 0, 0, 1, 2, 2, 0, 1, 2, 1, 2, 0, 2, 2, 2, 0, 2,
2, 1, 2, 2, 0, 2, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 0, 2, 1, 1, 0, 2,
1, 1, 2, 0, 2, 1, 0, 2, 0, 0, 2, 0, 0, 2, 0, 1, 1, 2, 2, 0, 1, 1,
0, 0, 1, 1, 2, 1, 0, 0, 1, 1])
from sklearn.tree import DecisionTreeClassifier
# 实例化决策树
clf = DecisionTreeClassifier()
# 训练模型
clf.fit(x_train,y_train)
DecisionTreeClassifier()
# 打印分类标签
clf.classes_
array([0, 1, 2])
# 打印分类中心
clf.feature_importances_
array([0. , 0.01666667, 0.96571429, 0.01761905])
# 对测试样本的标签进行预测
predict = clf.predict(x_test)
predict
array([1, 2, 1, 2, 1, 0, 2, 2, 0, 1, 0, 2, 1, 0, 1, 2, 1, 1, 0, 1, 0, 2,
2, 0, 0, 1, 0, 0, 1, 1])
y_test
array([1, 2, 2, 2, 1, 0, 2, 2, 0, 2, 0, 2, 1, 0, 2, 1, 1, 1, 0, 1, 0, 2,
2, 0, 0, 1, 0, 0, 1, 1])
(predict==y_test).mean()
0.8666666666666667
from sklearn.metrics import classification_report
# 打印模型评估报告
report = classification_report(y_test,predict)
print(report)
precision recall f1-score support
0 1.00 1.00 1.00 10
1 0.75 0.90 0.82 10
2 0.88 0.70 0.78 10
accuracy 0.87 30
macro avg 0.88 0.87 0.87 30
weighted avg 0.88 0.87 0.87 30
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
boston = load_boston()
print(boston.keys())
dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])
boston['data'].shape
(506, 13)
# 分割数据
x_train,x_test,y_train,y_test=train_test_split(
boston['data'],boston['target'],
test_size=0.2)
# 构建并训练模型
model = LinearRegression().fit(x_train,y_train)
# 访问模型属性
model.coef_
array([-1.18725169e-01, 4.87096337e-02, 8.41467516e-03, 2.47675784e+00,
-2.05579889e+01, 3.17620416e+00, 4.16738730e-03, -1.51508528e+00,
3.12918535e-01, -1.13958502e-02, -9.93966517e-01, 9.29124942e-03,
-4.98433972e-01])
# 查看截距
model.intercept_
42.01199982392576
import matplotlib.pyplot as plt
predict = model.predict(x_test)
# 评估模型方式一
plt.figure()
plt.plot(range(len(y_test)),predict)
plt.plot(range(len(y_test)),y_test,'r-.')
plt.show()
from sklearn.metrics import mean_squared_error
# 模型评估方式二
mean_squared_error(y_test,predict)
23.850199845955